Definition
Plain language
A method that fills an AI's silent reasoning slots all at once over a few rounds, instead of computing them slowly one after another.
As stated in the literature
Approximate Parallel Latent Refinement — uses Jacobi-style parallel updates over causally-ordered latent reasoning slots, guaranteeing the first K slots are exact after K rounds and decoupling reasoning capacity from sequential compute.
Also called: Approximate Parallel Latent Refinement
Why it matters: It separates how much a model can reason from how long it has to wait, letting agents think more deeply without a proportional slowdown.
For example, instead of computing five hidden reasoning steps one slot at a time, the model guesses all five at once and sharpens them over a few quick passes.
Heard on the show
“The authors call it APLR — Approximate Parallel Latent Refinement.”Episode 115 — Teaching a Phone Agent to Reason Silently, And Keeping It Honest